MediaEval 2015 - Multi-Scale Approaches to the MediaEval 2015 "Emotion in Music" Task
MediaEval 2015 - Recod @ MediaEval 2015: Diverse Social Images Retrieval
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Recod @ MediaEval 2015: Diverse Social Images Retrieval Rodrigo T. Calumby , Iago B. A. do C. Araujo, Vinícius P. Santana Javier A. V. Munoz, Otávio A. B. Penatti, Lin T. Li, Jurandy Almeida Giovani Chiachia, Marcos A. Gonçalves, and Ricardo da S. Torres Acknowledgments: UEFS/PROBIC, FAPESP, and MediaEval 2015 Organizers UEFS contact: [email protected] Daniel Moreira on behalf of
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Transcript of MediaEval 2015 - Recod @ MediaEval 2015: Diverse Social Images Retrieval
Recod @ MediaEval 2015:Diverse Social Images Retrieval
Rodrigo T. Calumby, Iago B. A. do C. Araujo, Vinícius P. SantanaJavier A. V. Munoz, Otávio A. B. Penatti, Lin T. Li, Jurandy AlmeidaGiovani Chiachia, Marcos A. Gonçalves, and Ricardo da S. Torres
Acknowledgments: UEFS/PROBIC, FAPESP, and MediaEval 2015 Organizers
UEFS
contact: [email protected]
Daniel Moreiraon behalf of
Face detectionGeographic
Visual / Textual / Credibility / Geo
input
output
Clustering
Representative selection
Filtering
Reranking
Diversification
Blur
Genetic programming
Fusion
Relevance-basedSelection
(up to 150-top ranked)
Filtering
Approach
Recod @ MediaEval 2015: Diverse Social Images Retrieval
Geo Filter
Face Filter#faces > 1 → non-relevant
(location: Christ the Redeemer, Rio de Janeiro)
10km radius limit from reference lat/long of the location
Blur Filter
(location: Iguazu Falls, Brazil/Argentina)
α > 0.8 → non-relevant
Filtering
Recod @ MediaEval 2015: Diverse Social Images Retrieval
Face detectionGeographic
Visual / Textual / Credibility / Geo
input
output
Clustering
Representative selection
Filtering
Reranking
Diversification
Blur
Genetic programming
Fusion
Relevance-basedSelection
(up to 150-top ranked)
Filtering
Approach
Recod @ MediaEval 2015: Diverse Social Images Retrieval
Recod @ MediaEval 2015: Diverse Social Images Retrieval
Reranking
Fusion
(Genetic Programming) { VisualRank1 TextRank CredRank GeoRank
CombSUM MRA
BordaCount
VisualRank2
CombSUM
Ranked List
INPUT
OUTPUT {
Original Re-ranked
(location: Casa Batlló, Barcelona)
Location representatives:
VISUAL
Textual query: Locality textCredibility: user scores GEOGRAPHIC
Recod @ MediaEval 2015: Diverse Social Images Retrieval
Parameter Value
Number of generations
30
Genetics operators
Reproduction, Mutation, Crossover
Fitness functions
FFP1, WAS, MAP, NDCG
Rank Agg. methods
CombMAX, CombMIN, CombSUM, CombMED, CombANZ, CombMNZ,
RLSim, BordaCount, RRF, MRA
Face detectionGeographic
Visual / Textual / Credibility / Geo
input
output
Clustering
Representative selection
Filtering
Reranking
Diversification
Blur
Genetic programming
Fusion
Relevance-basedSelection
(up to 150-top ranked)
Filtering
Approach
Recod @ MediaEval 2015: Diverse Social Images Retrieval
Clustering
Selection- Descending cluster size- Best connected items from each cluster
(location: Arc de Triomphe, Paris)
kMedoids: 30 to 40 clustersInitial medoids: rank offset positions
Output list
Recod @ MediaEval 2015: Diverse Social Images Retrieval
Run Filtering Reranking Diversity
1 Geo*, face, blur Visual* BIC
2 Geo*, face, blur Textual Cosine + Jaccard
3 Geo*, face, blur Visual*, textual Jaccard
4 Geo*, face, blur Credibility Users
5 Geo*, face*, blur Visual*, textual, credibility, geo* Jaccard
*only for one-topic queries
Submitted Runs
Recod @ MediaEval 2015: Diverse Social Images Retrieval
Recod @ MediaEval 2015:Diverse Social Images Retrieval
Rodrigo T. Calumby, Iago B. A. do C. Araujo, Vinícius P. SantanaJavier A. V. Munoz, Otávio A. B. Penatti, Lin T. Li, Jurandy AlmeidaGiovani Chiachia, Marcos A. Gonçalves, and Ricardo da S. Torres
Acknowledgments: UEFS/PROBIC, FAPESP, and MediaEval 2015 Organizers
UEFS
contact: [email protected]
Thank you!
